Scaling Voice Data
for Africa.
Most African languages have less than 50 hours of speech data. We use state-of-the-art voice cloning to scale that to hundreds—no recording studios required.
The Low-Resource Gap
African languages are severely underrepresented in speech technology. Robust ASR and TTS systems require thousands of hours of data.
Traditional collection is expensive and logistically hard. As a result, millions of speakers are left behind by modern AI tools.
Data disparity ratio: 200:1
Synthetic Augmentation
We use voice cloning to generate diverse synthetic speakers from limited source recordings, scaling datasets while preserving linguistic accuracy.
1. Collect Baseline
Gather high-quality single-speaker recordings (approx. 10 hours) as ground truth data.
2. Clone Voices
Generate hundreds of synthetic speaker identities to read text from the target language corpus.
3. Train Models
Use the combined real and synthetic dataset to train robust ASR models that generalize well.
Target Languages
Current focus languages for dataset creation